Good days and bad days in dementia: a qualitative chart review of variable symptom expression
Bibliographic record
Abstract
BACKGROUND: Despite its importance in the lived experience of dementia, symptom fluctuation has been little studied outside Lewy body dementia. We aimed to characterize symptom fluctuation in patients with Alzheimer's disease (AD) and mixed dementia. METHODS: A qualitative analysis of health records that included notations on good days and bad days yielded 52 community-dwelling patients (women, n = 30; aged 39-91 years; mild dementia, n = 26, chiefly AD, n = 36). RESULTS: Good days/bad days were most often described as changes in the same core set of symptoms (e.g. less/more verbal repetition). In other cases, only good or only bad days were described (e.g., no bad days, better sense of humor on good days). Good days were typically associated with improved global cognition, function, interest, and initiation. Bad days were associated with frequent verbal repetition, poor memory, increased agitation and other disruptive behaviors. CONCLUSIONS: Clinically important variability in symptoms appears common in AD and mixed dementia. Even so, what makes a day "good" is not simply more (or less) of what makes a day "bad". Further investigation of the factors that facilitate or encourage good days and mitigate bad days may help improve quality of life for patients and caregivers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".